The OEE metrics in QAD BI attempt to simplify complicated production issues via simpler, more intuitive graphs of performance that can be used to trace problems back to their root causes—whether they are downtime, excessive setup, efficiency problems, scrap and rework, or lack of work.

The kinds of issues that can be identified using OEE include:

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Bottlenecks. Identify which resources cannot perform up to expectations and therefore are creating bottlenecks for the entire facility.

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Downtime. Identify which resources are down constantly, generally not available when needed, or require a setup that is so excessive that production capacity is compromised.

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Unused or underutilized capacity. Identify which resources have potential capacity improvements that do not require buying more equipment or hiring more people. Determine if you can increase through productivity gains rather than more machines.

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Quality. Determine if you have improved equipment availability or improved efficiency at the expense of defect levels. Identify if improvement efforts and investments can be better targeted to reducing scrap rates rather than increasing efficiency or reducing downtime.

Specific questions that can be answered using OEE include:

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How productive are we? In each of our major manufacturing or supply chain resources? Overall? What percentage of our hours is value adding? What is our equipment utilization?

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What kind of downtime are we experiencing in our key resources or work centers? What is the equipment availability?

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How do the key resources or lines perform against our standards?

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What is our current quality performance in key resources or departments? By work center or production line, by item? How much scrap? In percentage terms? Dollars?

Generally speaking, the goal of OEE programs is to reduce and eliminate the most common causes of productivity loss in manufacturing, which include:

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Breakdowns. Eliminating unplanned downtime is critical to improving OEE. Other OEE factors cannot be addressed if the process is down. It is not only important to know how much downtime your process is experiencing (and when) but also to be able to attribute the lost time to the specific source or reason for the loss. With downtime statistics and supporting data, root-cause analysis can trace back to the most severe loss categories.

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Setup and Adjustment Time. Setup and adjustment time is generally measured as the time between the last good part produced before setup, to the first consistent good parts produced after setup. This often includes substantial adjustment and/or warm-up time to consistently produce parts that meet quality standards.

Tracking Actual Setup Time is critical to reducing this loss, together with an active program to reduce this time (such as an SMED – Single Minute Exchange of Dies program).

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Minor Stoppages, Reduced Speeds. Minor Stoppages and Reduced Speed are often the most difficult of the losses to monitor and record, especially when the data depends on discrete time reporting by operation. When reporting is done via labor reporting transactions rather than from backflushing at standard, Cycle Time Analysis can be utilized to pinpoint these loss types.

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Startup Rejects and Production Rejects. Startup rejects and production rejects are different and the root causes are often different between startup and steady-state production. Tracking when rejects occur during the process—say immediately following setup—can help pinpoint potential causes and drive improved corrective action.